Leverage RAF to find domain experts on research social network services : A big data analytics methodology with MapReduce framework

Abstract With the rapid proliferation of information technology, the increasing amount of information available has posted significant challenges on relevant information discovery for users. An alternative way is to find an expert with specific expertise. Expert recommendation is important in variety of contexts ranging from industry to academia. Information retrieval methods or graph-based methods have been proposed to approach this problem in previous research while some important contextual factors are ignored. In this paper, considering the factors of topic relevance, expert quality, and researcher connectivity, we propose a novel researcher modeling approach to recommend experts in scientific communities. The proposed recommendation method is well evaluated and compared with some commonly used recommendation models. Furthermore, the proposed method has been implemented in ScholarMate (www.scholarmate.com), an online research social network platform. The experimental results exhibit that the proposed method is more effective than baseline methods, and it is a potential recommendation method to find domain experts on research social network services.